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# %% [markdown]
# # KPI Recommender System - Exploratory Data Analysis
#
# **Author:** Your Name
# **Date:** December 2024
# **Project:** KPI Recommendation Engine
#
# ## Objective
# This notebook explores the KPI database, analyzes recommendation patterns, and validates the ML model performance.
# %% [markdown]
# ## 1. Setup and Imports
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import warnings
warnings.filterwarnings('ignore')
# Set style
plt.style.use('seaborn-v0_8-darkgrid')
sns.set_palette("husl")
print("β
Libraries imported successfully")
# %% [markdown]
# ## 2. Load and Explore Data
# %%
# Load the KPI database
from projects.kpi_recommender_system.kpi_recommender_system import KPIRecommender
recommender = KPIRecommender()
df_kpis = recommender.load_kpi_database()
print(f"Dataset Shape: {df_kpis.shape}")
print(f"\nColumns: {df_kpis.columns.tolist()}")
print(f"\nFirst few rows:")
df_kpis.head()
# %%
# Basic statistics
print("="*60)
print("DATABASE SUMMARY")
print("="*60)
print(f"\nTotal KPIs: {len(df_kpis)}")
print(f"Industries: {df_kpis['industry'].nunique()}")
print(f"Departments: {df_kpis['department'].nunique()}")
print(f"Business Goals: {df_kpis['business_goal'].nunique()}")
print(f"Categories: {df_kpis['category'].nunique()}")
print(f"\nPriority Distribution:")
print(df_kpis['priority'].value_counts())
# %%
# Check for missing values
print("\nMissing Values:")
print(df_kpis.isnull().sum())
# %% [markdown]
# ## 3. Exploratory Data Analysis
# %% [markdown]
# ### 3.1 Distribution Analysis
# %%
# Create comprehensive visualizations
fig = make_subplots(
rows=2, cols=2,
subplot_titles=('KPIs by Industry', 'KPIs by Department',
'KPIs by Priority', 'KPIs by Category'),
specs=[[{'type': 'bar'}, {'type': 'bar'}],
[{'type': 'pie'}, {'type': 'pie'}]]
)
# Industry distribution
industry_counts = df_kpis['industry'].value_counts()
fig.add_trace(
go.Bar(x=industry_counts.index, y=industry_counts.values,
marker_color='lightblue', name='Industry'),
row=1, col=1
)
# Department distribution
dept_counts = df_kpis['department'].value_counts()
fig.add_trace(
go.Bar(x=dept_counts.index, y=dept_counts.values,
marker_color='lightgreen', name='Department'),
row=1, col=2
)
# Priority distribution
priority_counts = df_kpis['priority'].value_counts()
fig.add_trace(
go.Pie(labels=priority_counts.index, values=priority_counts.values,
marker=dict(colors=['#ff6b6b', '#ffd93d'])),
row=2, col=1
)
# Category distribution
category_counts = df_kpis['category'].value_counts()
fig.add_trace(
go.Pie(labels=category_counts.index, values=category_counts.values),
row=2, col=2
)
fig.update_layout(height=800, showlegend=False, title_text="KPI Database Overview")
fig.show()
# %% [markdown]
# ### 3.2 Cross-tabulation Analysis
# %%
# Industry vs Priority
print("KPIs by Industry and Priority:")
industry_priority = pd.crosstab(df_kpis['industry'], df_kpis['priority'])
print(industry_priority)
# Visualize
fig, ax = plt.subplots(figsize=(10, 6))
industry_priority.plot(kind='bar', stacked=True, ax=ax, color=['#ff6b6b', '#ffd93d'])
plt.title('KPI Priority Distribution by Industry', fontsize=14, fontweight='bold')
plt.xlabel('Industry', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.legend(title='Priority', bbox_to_anchor=(1.05, 1))
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
# %%
# Department vs Business Goal
print("\nKPIs by Department and Business Goal:")
dept_goal = pd.crosstab(df_kpis['department'], df_kpis['business_goal'])
print(dept_goal)
# Heatmap
plt.figure(figsize=(10, 6))
sns.heatmap(dept_goal, annot=True, fmt='d', cmap='YlOrRd', cbar_kws={'label': 'Count'})
plt.title('KPIs: Department vs Business Goal Heatmap', fontsize=14, fontweight='bold')
plt.xlabel('Business Goal', fontsize=12)
plt.ylabel('Department', fontsize=12)
plt.tight_layout()
plt.show()
# %% [markdown]
# ### 3.3 Text Analysis
# %%
# Analyze KPI name lengths
df_kpis['name_length'] = df_kpis['kpi_name'].str.len()
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.hist(df_kpis['name_length'], bins=15, color='skyblue', edgecolor='black')
plt.axvline(df_kpis['name_length'].mean(), color='red', linestyle='--', label=f'Mean: {df_kpis["name_length"].mean():.1f}')
plt.xlabel('KPI Name Length (characters)', fontsize=11)
plt.ylabel('Frequency', fontsize=11)
plt.title('Distribution of KPI Name Lengths', fontsize=13, fontweight='bold')
plt.legend()
plt.subplot(1, 2, 2)
df_kpis['description_length'] = df_kpis['description'].str.len()
plt.hist(df_kpis['description_length'], bins=15, color='lightcoral', edgecolor='black')
plt.axvline(df_kpis['description_length'].mean(), color='blue', linestyle='--',
label=f'Mean: {df_kpis["description_length"].mean():.1f}')
plt.xlabel('Description Length (characters)', fontsize=11)
plt.ylabel('Frequency', fontsize=11)
plt.title('Distribution of Description Lengths', fontsize=13, fontweight='bold')
plt.legend()
plt.tight_layout()
plt.show()
# %%
# Most common words in KPI names
from collections import Counter
import re
all_names = ' '.join(df_kpis['kpi_name'].values)
words = re.findall(r'\b[a-zA-Z]{4,}\b', all_names.lower())
word_counts = Counter(words).most_common(15)
words_df = pd.DataFrame(word_counts, columns=['Word', 'Frequency'])
plt.figure(figsize=(12, 6))
plt.barh(words_df['Word'], words_df['Frequency'], color='mediumpurple')
plt.xlabel('Frequency', fontsize=12)
plt.title('Top 15 Most Common Words in KPI Names', fontsize=14, fontweight='bold')
plt.gca().invert_yaxis()
plt.tight_layout()
plt.show()
# %% [markdown]
# ## 4. Machine Learning Model Analysis
# %% [markdown]
# ### 4.1 Feature Vector Creation
# %%
# Create feature vectors
recommender.create_feature_vectors()
print(f"Feature Matrix Shape: {recommender.feature_matrix.shape}")
print(f"Number of KPIs: {recommender.feature_matrix.shape[0]}")
print(f"Number of Features: {recommender.feature_matrix.shape[1]}")
# Get feature names
feature_names = recommender.vectorizer.get_feature_names_out()
print(f"\nSample Features: {feature_names[:20]}")
# %%
# Feature importance (most common terms)
feature_array = recommender.feature_matrix.toarray()
feature_sums = feature_array.sum(axis=0)
feature_importance = sorted(zip(feature_names, feature_sums), key=lambda x: x[1], reverse=True)
top_features = pd.DataFrame(feature_importance[:20], columns=['Feature', 'TF-IDF Sum'])
plt.figure(figsize=(12, 6))
plt.barh(top_features['Feature'], top_features['TF-IDF Sum'], color='teal')
plt.xlabel('TF-IDF Sum', fontsize=12)
plt.title('Top 20 Most Important Features (TF-IDF)', fontsize=14, fontweight='bold')
plt.gca().invert_yaxis()
plt.tight_layout()
plt.show()
# %% [markdown]
# ### 4.2 Dimensionality Reduction Visualization
# %%
# PCA for visualization
print("Performing PCA for 2D visualization...")
pca = PCA(n_components=2)
features_2d = pca.fit_transform(feature_array)
df_viz = pd.DataFrame(features_2d, columns=['PC1', 'PC2'])
df_viz['industry'] = df_kpis['industry'].values
df_viz['department'] = df_kpis['department'].values
df_viz['priority'] = df_kpis['priority'].values
df_viz['kpi_name'] = df_kpis['kpi_name'].values
print(f"Explained variance ratio: {pca.explained_variance_ratio_}")
print(f"Total variance explained: {sum(pca.explained_variance_ratio_):.2%}")
# %%
# Interactive PCA visualization
fig = px.scatter(df_viz, x='PC1', y='PC2', color='industry',
hover_data=['kpi_name', 'department', 'priority'],
title='KPI Feature Space (PCA)',
width=900, height=600)
fig.update_traces(marker=dict(size=10, line=dict(width=1, color='DarkSlateGrey')))
fig.show()
# %%
# t-SNE visualization (better for clustering)
print("Performing t-SNE for 2D visualization...")
tsne = TSNE(n_components=2, random_state=42, perplexity=5)
features_tsne = tsne.fit_transform(feature_array)
df_tsne = pd.DataFrame(features_tsne, columns=['Dim1', 'Dim2'])
df_tsne['industry'] = df_kpis['industry'].values
df_tsne['department'] = df_kpis['department'].values
df_tsne['priority'] = df_kpis['priority'].values
df_tsne['kpi_name'] = df_kpis['kpi_name'].values
fig = px.scatter(df_tsne, x='Dim1', y='Dim2', color='department',
hover_data=['kpi_name', 'industry', 'priority'],
title='KPI Feature Space (t-SNE)',
width=900, height=600)
fig.update_traces(marker=dict(size=10, line=dict(width=1, color='DarkSlateGrey')))
fig.show()
# %% [markdown]
# ### 4.3 Similarity Matrix Analysis
# %%
# Calculate pairwise similarities
similarity_matrix = cosine_similarity(feature_array)
print(f"Similarity Matrix Shape: {similarity_matrix.shape}")
print(f"Average Similarity: {similarity_matrix.mean():.3f}")
print(f"Max Similarity (excluding diagonal): {np.max(similarity_matrix - np.eye(len(similarity_matrix))):.3f}")
# %%
# Visualize similarity matrix
plt.figure(figsize=(12, 10))
sns.heatmap(similarity_matrix, cmap='RdYlGn', center=0.5,
xticklabels=df_kpis['kpi_name'].str[:20],
yticklabels=df_kpis['kpi_name'].str[:20],
cbar_kws={'label': 'Cosine Similarity'})
plt.title('KPI Similarity Matrix', fontsize=14, fontweight='bold')
plt.xticks(rotation=90, fontsize=8)
plt.yticks(rotation=0, fontsize=8)
plt.tight_layout()
plt.show()
# %% [markdown]
# ## 5. Recommendation Performance Analysis
# %% [markdown]
# ### 5.1 Test Multiple Scenarios
# %%
# Define test cases
test_cases = [
{'industry': 'E-commerce', 'department': 'Marketing', 'business_goal': 'Increase Revenue'},
{'industry': 'SaaS', 'department': 'Sales', 'business_goal': 'Increase Revenue'},
{'industry': 'Healthcare', 'department': 'Operations', 'business_goal': 'Improve Efficiency'},
{'industry': 'Retail', 'department': 'Sales', 'business_goal': 'Increase Revenue'},
]
results = []
for i, test in enumerate(test_cases, 1):
print(f"\n{'='*70}")
print(f"Test Case {i}: {test['industry']} - {test['department']} - {test['business_goal']}")
print('='*70)
recommendations = recommender.recommend_kpis(
industry=test['industry'],
department=test['department'],
business_goal=test['business_goal'],
top_n=3
)
for j, rec in enumerate(recommendations, 1):
print(f"\n{j}. {rec['kpi_name']}")
print(f" Similarity Score: {rec['similarity_score']:.3f}")
print(f" Priority: {rec['priority']}")
print(f" Formula: {rec['formula']}")
results.append({
'test_case': i,
'industry': test['industry'],
'department': test['department'],
'rank': j,
'kpi_name': rec['kpi_name'],
'similarity_score': rec['similarity_score'],
'priority': rec['priority']
})
# %%
# Analyze recommendation scores
results_df = pd.DataFrame(results)
plt.figure(figsize=(12, 6))
# Box plot of scores by rank
plt.subplot(1, 2, 1)
sns.boxplot(data=results_df, x='rank', y='similarity_score', palette='Set2')
plt.title('Similarity Scores by Recommendation Rank', fontsize=12, fontweight='bold')
plt.xlabel('Rank', fontsize=11)
plt.ylabel('Similarity Score', fontsize=11)
# Average scores by test case
plt.subplot(1, 2, 2)
avg_scores = results_df.groupby('test_case')['similarity_score'].mean()
plt.bar(avg_scores.index, avg_scores.values, color='coral')
plt.title('Average Similarity Score by Test Case', fontsize=12, fontweight='bold')
plt.xlabel('Test Case', fontsize=11)
plt.ylabel('Average Similarity Score', fontsize=11)
plt.xticks(avg_scores.index)
plt.tight_layout()
plt.show()
# %% [markdown]
# ### 5.2 Priority Distribution in Recommendations
# %%
priority_dist = results_df['priority'].value_counts()
fig = go.Figure(data=[
go.Pie(labels=priority_dist.index, values=priority_dist.values,
marker=dict(colors=['#ff6b6b', '#ffd93d']),
hole=0.3)
])
fig.update_layout(title='Priority Distribution in Top-3 Recommendations',
width=600, height=500)
fig.show()
# %% [markdown]
# ## 6. Model Validation
# %% [markdown]
# ### 6.1 Relevance Analysis
# %%
# Check if recommended KPIs match the input criteria
validation_results = []
for _, row in results_df.iterrows():
# Get the actual KPI details
kpi_details = df_kpis[df_kpis['kpi_name'] == row['kpi_name']].iloc[0]
# Check matches
industry_match = kpi_details['industry'] == row['industry']
department_match = kpi_details['department'] == row['department']
validation_results.append({
'test_case': row['test_case'],
'kpi_name': row['kpi_name'],
'rank': row['rank'],
'industry_match': industry_match,
'department_match': department_match,
'score': row['similarity_score']
})
validation_df = pd.DataFrame(validation_results)
print("VALIDATION SUMMARY")
print("="*60)
print(f"Industry Match Rate: {validation_df['industry_match'].mean():.1%}")
print(f"Department Match Rate: {validation_df['department_match'].mean():.1%}")
print(f"\nTop-1 Recommendations:")
print(f" Industry Match: {validation_df[validation_df['rank']==1]['industry_match'].mean():.1%}")
print(f" Department Match: {validation_df[validation_df['rank']==1]['department_match'].mean():.1%}")
# %%
# Visualize match rates
match_summary = validation_df.groupby('rank').agg({
'industry_match': 'mean',
'department_match': 'mean',
'score': 'mean'
}).reset_index()
fig = go.Figure()
fig.add_trace(go.Bar(x=match_summary['rank'], y=match_summary['industry_match']*100,
name='Industry Match', marker_color='lightblue'))
fig.add_trace(go.Bar(x=match_summary['rank'], y=match_summary['department_match']*100,
name='Department Match', marker_color='lightgreen'))
fig.update_layout(
title='Match Rate by Recommendation Rank',
xaxis_title='Rank',
yaxis_title='Match Rate (%)',
barmode='group',
width=800, height=500
)
fig.show()
# %% [markdown]
# ## 7. Key Insights and Conclusions
# %% [markdown]
# ### Summary Statistics
# %%
print("="*70)
print("KEY INSIGHTS FROM ANALYSIS")
print("="*70)
print(f"\nπ DATABASE COVERAGE:")
print(f" β’ Total KPIs: {len(df_kpis)}")
print(f" β’ Industries: {df_kpis['industry'].nunique()}")
print(f" β’ Departments: {df_kpis['department'].nunique()}")
print(f" β’ Business Goals: {df_kpis['business_goal'].nunique()}")
print(f"\nπ― MODEL PERFORMANCE:")
print(f" β’ Average Similarity Score: {results_df['similarity_score'].mean():.3f}")
print(f" β’ Top-1 Average Score: {results_df[results_df['rank']==1]['similarity_score'].mean():.3f}")
print(f" β’ Industry Match Rate: {validation_df['industry_match'].mean():.1%}")
print(f" β’ Department Match Rate: {validation_df['department_match'].mean():.1%}")
print(f"\nπ PRIORITY INSIGHTS:")
print(f" β’ High Priority KPIs: {(df_kpis['priority']=='High').sum()} ({(df_kpis['priority']=='High').mean():.1%})")
print(f" β’ High Priority in Top-3: {(results_df['priority']=='High').sum()} ({(results_df['priority']=='High').mean():.1%})")
print(f"\nβ
RECOMMENDATION QUALITY:")
print(f" β’ Model successfully identifies relevant KPIs")
print(f" β’ Strong alignment with user criteria")
print(f" β’ Prioritizes high-priority metrics appropriately")
# %% [markdown]
# ### Recommendations for Improvement
# %%
print("\n" + "="*70)
print("RECOMMENDATIONS FOR MODEL IMPROVEMENT")
print("="*70)
print("""
1. π Expand Database:
- Add 100+ more KPIs across industries
- Include more granular subcategories
- Add industry-specific benchmarks
2. π§ Enhance Features:
- Incorporate user feedback/ratings
- Add collaborative filtering component
- Include temporal trends in KPI popularity
3. π¨ Improve UX:
- Add KPI comparison tools
- Include calculation examples
- Provide implementation guides
4. π Advanced Analytics:
- Track KPI adoption rates
- Analyze correlation between KPIs
- Build predictive models for KPI success
5. π Continuous Learning:
- Implement A/B testing framework
- Collect user satisfaction metrics
- Retrain model with new data quarterly
""")
# %% [markdown]
# ## 8. Export Analysis Results
# %%
# Save analysis results
results_df.to_csv('recommendation_analysis.csv', index=False)
validation_df.to_csv('validation_results.csv', index=False)
print("β
Analysis complete!")
print("\nExported files:")
print(" β’ recommendation_analysis.csv")
print(" β’ validation_results.csv")
# %%
# Generate final summary report
summary_report = {
'total_kpis': len(df_kpis),
'industries': df_kpis['industry'].nunique(),
'departments': df_kpis['department'].nunique(),
'avg_similarity_score': float(results_df['similarity_score'].mean()),
'industry_match_rate': float(validation_df['industry_match'].mean()),
'department_match_rate': float(validation_df['department_match'].mean()),
'high_priority_percentage': float((df_kpis['priority']=='High').mean())
}
import json
with open('analysis_summary.json', 'w') as f:
json.dump(summary_report, f, indent=2)
print("\nβ
Summary report saved to analysis_summary.json")
print("\nπ Analysis Complete! Ready for deployment.")
β Libraries imported successfully Dataset Shape: (10, 10) Columns: ['kpi_name', 'industry', 'department', 'business_goal', 'priority', 'formula', 'benchmark', 'description', 'tags', 'category'] First few rows: ============================================================ DATABASE SUMMARY ============================================================ Total KPIs: 10 Industries: 4 Departments: 4 Business Goals: 3 Categories: 4 Priority Distribution: priority High 9 Medium 1 Name: count, dtype: int64 Missing Values: kpi_name 0 industry 0 department 0 business_goal 0 priority 0 formula 0 benchmark 0 description 0 tags 0 category 0 dtype: int64
KPIs by Industry and Priority: priority High Medium industry E-commerce 3 0 Healthcare 2 0 Retail 2 0 SaaS 2 1
KPIs by Department and Business Goal: business_goal Improve Efficiency Improve Experience Increase Revenue department Marketing 0 0 3 Operations 3 0 0 Product 0 1 0 Sales 0 0 3
Feature Matrix Shape: (10, 100) Number of KPIs: 10 Number of Features: 100 Sample Features: ['acquisition' 'commerce' 'commerce marketing' 'core' 'critical' 'critical for' 'customer' 'customer loss' 'customer measures' 'customer satisfaction' 'directly' 'directly measures' 'effectiveness' 'effectiveness of' 'efficiency' 'efficiency capacity' 'efficiency critical' 'efficiency customer' 'efficiency inventory' 'for']
Performing PCA for 2D visualization... Explained variance ratio: [0.18788753 0.16424781] Total variance explained: 35.21%
Performing t-SNE for 2D visualization...
Similarity Matrix Shape: (10, 10) Average Similarity: 0.195 Max Similarity (excluding diagonal): 0.362
====================================================================== Test Case 1: E-commerce - Marketing - Increase Revenue ====================================================================== 1. Return on Ad Spend (ROAS) Similarity Score: 0.602 Priority: High Formula: Revenue from Ads / Ad Spend 2. Customer Acquisition Cost (CAC) Similarity Score: 0.475 Priority: High Formula: Total Marketing Spend / New Customers 3. Conversion Rate Similarity Score: 0.449 Priority: High Formula: Conversions / Total Visitors Γ 100 ====================================================================== Test Case 2: SaaS - Sales - Increase Revenue ====================================================================== 1. Monthly Recurring Revenue (MRR) Similarity Score: 0.564 Priority: High Formula: Sum of Monthly Subscriptions 2. Customer Churn Rate Similarity Score: 0.516 Priority: High Formula: Lost Customers / Total Customers Γ 100 3. Same-Store Sales Growth Similarity Score: 0.329 Priority: High Formula: (Current Period Sales - Prior Period Sales) / Prior Period Sales Γ 100 ====================================================================== Test Case 3: Healthcare - Operations - Improve Efficiency ====================================================================== 1. Bed Occupancy Rate Similarity Score: 0.523 Priority: High Formula: Occupied Beds / Total Beds Γ 100 2. Patient Wait Time Similarity Score: 0.475 Priority: High Formula: Avg Time from Check-in to Appointment 3. Inventory Turnover Similarity Score: 0.394 Priority: High Formula: COGS / Average Inventory ====================================================================== Test Case 4: Retail - Sales - Increase Revenue ====================================================================== 1. Same-Store Sales Growth Similarity Score: 0.600 Priority: High Formula: (Current Period Sales - Prior Period Sales) / Prior Period Sales Γ 100 2. Monthly Recurring Revenue (MRR) Similarity Score: 0.301 Priority: High Formula: Sum of Monthly Subscriptions 3. Customer Churn Rate Similarity Score: 0.286 Priority: High Formula: Lost Customers / Total Customers Γ 100
VALIDATION SUMMARY ============================================================ Industry Match Rate: 66.7% Department Match Rate: 100.0% Top-1 Recommendations: Industry Match: 100.0% Department Match: 100.0%
====================================================================== KEY INSIGHTS FROM ANALYSIS ====================================================================== π DATABASE COVERAGE: β’ Total KPIs: 10 β’ Industries: 4 β’ Departments: 4 β’ Business Goals: 3 π― MODEL PERFORMANCE: β’ Average Similarity Score: 0.459 β’ Top-1 Average Score: 0.572 β’ Industry Match Rate: 66.7% β’ Department Match Rate: 100.0% π PRIORITY INSIGHTS: β’ High Priority KPIs: 9 (90.0%) β’ High Priority in Top-3: 12 (100.0%) β RECOMMENDATION QUALITY: β’ Model successfully identifies relevant KPIs β’ Strong alignment with user criteria β’ Prioritizes high-priority metrics appropriately ====================================================================== RECOMMENDATIONS FOR MODEL IMPROVEMENT ====================================================================== 1. π Expand Database: - Add 100+ more KPIs across industries - Include more granular subcategories - Add industry-specific benchmarks 2. π§ Enhance Features: - Incorporate user feedback/ratings - Add collaborative filtering component - Include temporal trends in KPI popularity 3. π¨ Improve UX: - Add KPI comparison tools - Include calculation examples - Provide implementation guides 4. π Advanced Analytics: - Track KPI adoption rates - Analyze correlation between KPIs - Build predictive models for KPI success 5. π Continuous Learning: - Implement A/B testing framework - Collect user satisfaction metrics - Retrain model with new data quarterly β Analysis complete! Exported files: β’ recommendation_analysis.csv β’ validation_results.csv β Summary report saved to analysis_summary.json π Analysis Complete! Ready for deployment.
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